Abstract
Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a finite memory, or “population” of individual solutions (points on the search landscape), each of which has a scalar value, or “fitness” attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population. A number of classes of Evolutionary Algorithms can (broadly speaking) be distinguished by the nature of the alphabet used to represent the search space and the specialisation of the various operators used, e.g. Genetic Algorithms (Holland, 1975: binary or finite discrete representations), Evolution Strategies (Rechenberg, 1973; Schwefel, 1981: real numbers), Evolutionary Programming (Fogel et al., 1966: real numbers), and Genetic Programming (Cramer, 1985; Koza, 1989: tree based representation of computer programs). Although not originally designed for function optimisation, genetic algorithms have been shown to demonstrate an impressive ability to locate optima in large, complex and noisy search spaces.
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Smith, J.E. (2002). Genetic Algorithms. In: Pardalos, P.M., Romeijn, H.E. (eds) Handbook of Global Optimization. Nonconvex Optimization and Its Applications, vol 62. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5362-2_9
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